Semantic-Preserving Linguistic Steganography by Pivot Translation and Semantic-Aware Bins Coding

Linguistic steganography (LS) aims to embed secret information into a highly encoded text for covert communication. It can be roughly divided two main categories, i.e., modification based LS (MLS) and generation (GLS). MLS embeds data by slightly modifying given without impairing the meaning of text, whereas GLS uses well trained language model directly generate carrying data. A common disadvantage methods is that embedding payload very small, whose return preserving semantic quality text. In contrast, enables hider large payload, which has pay high price uncontrollable semantics. this paper, we propose novel method modify pivoting it between different languages using semantic-aware encoding strategy. Our purpose alter expression enabling embedded while keeping unchanged. Experiments have shown proposed work not only achieves but also shows superior performance in maintaining consistency resisting linguistic steganalysis.